An adaptive time-integration scheme for stiff chemistry based on Computational Singular Perturbation and Artificial Neural Networks

Riccardo Malpica Galassi, Pietro P. Ciottoli, Mauro Valorani, Hong G. Im

Research output: Contribution to journalArticlepeer-review

Abstract

We leverage the computational singular perturbation (CSP) theory to develop an adaptive time-integration scheme for stiff chemistry based on a local, projection-based, reduced order model (ROM) freed of the fast time-scales. Its construction is such that artificial neural networks (ANN) can be plugged-in as cheap surrogates of the local projection basis, which is a state function, to alleviate the computational cost, without sacrificing the geometrical and physical foundation of the method. In fact, the solver relies on the synthetic basis in place of the more expensive on-the-fly calculated basis, i.e. the eigenvectors of the Jacobian matrix of the chemical source term, to define the local slow invariant manifold (SIM) and the projection matrix, then integrates explicitly the projected, i.e., non-stiff, chemical source term.
Original languageEnglish (US)
Pages (from-to)110875
JournalJournal of Computational Physics
DOIs
StatePublished - Nov 29 2021

ASJC Scopus subject areas

  • Physics and Astronomy (miscellaneous)
  • Computer Science Applications

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